Cross-Cell Predictability of Lithium-Ion Battery Thermal Runaway: Short-Horizon Forecasting and Transient Predictability Loss During Rapid Heating

Reliable management of lithium-ion battery thermal runaway requires knowing not only when abnormal heating begins, but also how far future temperature evolution can be predicted with useful accuracy. This study evaluates that question using 67 abuse experiments from six cell/source families under strict leave-one-family-out validation. An interpretable temperature-only forecasting model was developed from the recent thermal trend and compared with four causal reference methods, including short-memory autoregressive and Kalman state-space approaches. Forecasting error increased markedly with prediction horizon: the mean experiment-normalized MAE of the proposed model was 2.09% at 1 s, 9.73% at 5 s, and 38.59% at 20 s. The median predictability horizons were 2 s and 4 s for 5% and 10% error tolerances, respectively. Forecastability also varied strongly within each event. Using an independent causal temperature-rate criterion, forecasting error reached a pronounced maximum around the transition into rapid heating and decreased after rapid escalation became established. This pattern remained consistent across alternative onset thresholds. These results indicate that, for the temperature-only cross-family setting examined here, deterministic forecasts are most reliable over short horizons and become particularly uncertain around rapid thermal escalation. The framework therefore provides an interpretable benchmark for assessing practical thermal-runaway forecastability rather than a universal limit on all possible forecasting methods.